Due to the aging population, falls are a major public health issue. Falls are the leading cause of injury-related death in older adults and falls can lead to chronic pain, disability, loss of independence, and high financial burden. Most falls occur during walking, and gait analyses have been used to predict those who are at greatest risk of falling. Higher risk of falling is associated with slower gait speed, increased stride time variability, increased step length variability, and increased step width variability. It is important to identify those at risk of falling so that professionals can provide interventions. However, these gait measures are not easily obtained; often a comprehensive gait analysis by a physical therapist is required.
Wide-ranging efforts have focused on identifying events that relate to gait and falling. Devices associated with gait and falling can be categorized into two broad categories: (1) fall detection alert systems and (2) analyzing and predicting future falls. The most common approach, the fall detection alert system, is designed to minimize ‘long-lie’ (i.e., monitoring the length of time a person is unable to get up after a fall) in order to ultimately reduce the amount of medical support the individual receives. However, the most common such system is the push-button method, which cannot be activated if the patient is unconscious. Thus, automated fall detection systems have been developed, including environment-based and wearable detectors. Typical designs involve several sensors. The most accurate devices use environment-based detection, incorporating embedded pressure sensors in the floor and video camera to monitor individuals' movement (Kistler Corp., Winterthur, Switzerland). However, this detection is limited to the instrumented environment and is costly. Wearable detectors, such as watch- or belt-type detectors, are not limited to a specific environment. These detectors often incorporate accelerometers and gyroscopes, monitor the acceleration magnitude and direction, in order detect falls and send an alert to an emergency service. These devices tend to have high false positive rates.
Environment-based and wearable fall detection systems, however, only provide useful data after the fall event, and associated injury, has already occurred. It is especially critical that systems are developed that identify those who are at the greatest risk of falling, so that preventive measures can be implemented and the fall and associated injuries can be avoided. Higher risk of falling is associated with slower gait speed, increased stride time variability, increased step length variability, and altered step width variability (Senden et al. 2012, Hausdorff et al. 2001, Brach et al. 2005, Moe-Nilssen and Helbostad 2005, Maki 1997).
Future fall risk can be predicted by assessing gait, which currently quantified by various tools, including both subjective and objective measures. These measures require a trained therapist, expensive equipment, and time-consuming analyses, so the measures cannot be adopted at a population level. Further, no single test is accepted by clinicians as a reference standard of fall risk. The lack of standard is based on the fact that falls are not caused by a single factor; the causes are multi-factorial, and include issues such as coordination, sensory acuity, cognitive ability, strength, visual ability, medications, and others. Assessment of a single factor or a set of factors is inadequate. However, the effects of these multi-factorial changes are observed in gait parameters, because balanced gait also relies on these factors. Gait analyses, however, are expensive and time-consuming.
Therefore, there is an unmet need for a device that can easily and quickly assess gait parameters. It is important to assess several parameters that have been empirically demonstrated to relate to fall risk: variability of step length, variability of step width, variability of step time, and gait speed. In addition, there is a need for a biofeedback device that will alert the wearer when their gait is compromised; such a device can also be used to provide gait retraining.